## STATISTICAL GRAPH

The title of each graph of the statistical study indicates the **parents variables (R or M & F)** to which the correlations are related. These correlations are represented by each point of the coloured lines corresponding to each examined **C** variable (**children**).

Likewise, the variables of unknown order, formed by the different **groups** of 1 to 10 **values** from the 70 **IQ values** of each parent and children variables are placed on the left hand side of the graph. The groups of 1 to 10 values located on the right hand side have been previously ordered with the variable mentioned at the bottom of the graph.

Indeed, an almost instantaneous perception of the exactitude of the particular specification of the statistical study is obtained; sixty **coefficients of determination** (r²) are shown in a way that highlights the global and underlying relations of the involved data set.

**See the methodology of the statistical abstract for more details**

## QUANTITATIVE STUDY COMMENTS

## 1. General statistical significance

The great increase of the correlation for the estimation of **homogenous groups** cannot be imputed to the reduction of 68 to 5 or 4 degrees of freedom, since the estimation with non-homogenous groups, without previous rearrangement, has the same degrees of freedom and the correlation even lowers with respect to the sample without grouping.

When the model of the *quantitative study* has more freedom with the two intelligence quotients' variables, **M** and **F,** either it definitely adjusts better by statistical effect or the statistical data set we have available is a particular case.

In general, the model of genetic evolution of intelligence (*Mendelian genetics – Conditional intelligence – Gobal Cognitive Theory*) adjusts perfectly, showing an **r² **superior to 0.9 in several cases. Bearing in mind the tendency to increase the goodness of fit with the size of rearranged groups, we could asume it would be **over 0,9 in almost all the cases **for groups bigger than 20, of course, it should be needed a bigger sample.

## 2. The *Global model* - Quantitative study on simulation of evolution with artificial quotients of intelligence.

We could say the *Social model* of evolution of intelligence resolves the debate** nature vs. nurture** in intelligence. It shows that there is not much margin left to deny the hereditary nature of intelligence, not even to try to reduce it to less than 80%. Of course, one could always argue that there is a problem with the concept of intelligence and why not? with the definition of environment.

The main goal of this **quantitative study** was not to resolve the debate** nature vs. nurture** in intelligence but to go further and demonstrate the operational existence of the *Genetic Information Verification* method (GIV) pointed out by the GTCEL (*General Theory of the Conditional Evolution of Life*) for the intelligence particular case.

Also, the *Social model* of evolution of intelligence has been useful to determine that the significant chromosome is the one of less intellectual power.

Due to the accuracy of the *quantitative study,* and the fact that I had all the elements to do its **computer simulation,** I thought it would be a good idea to use it in order to confirm the results in despite the **complexity** of the task..

The computer simulation within the **Global model** should generate artificial intelligence quotients that should behave like those observed. This task was **much more complicated** than I thought, forcing me to **eliminate all the simplifications** that I had introduced in the model design.

Finally, after introducing the functional limitations, the *Global model *works satisfactorily which can be verified with the images associated with the graphs.

Of course, to obtain a satisfactory optical effect, the images have been chosen where **W** shows better adjustments to one of the **C** variables. It could be said that the graphs are speak for themselves.

Comparing the statistical correlation an regression with original variables and with centred variables, the GMCI with centred variables is higher in both cases and increases more when the dependent variables or objective function is **M & F** than with **R°**

The statistical study of the *Global model* **absolutly confirms** the results of the *Social model* about the debate** nature vs. nurture** in intelligence

## 3. Significant figures of this particular graph of the quantitative study.

As you can clearly see by its form, the four dependent variables of the children, analyzed in the model, behave in a very similar way to the progenitors' explanatory variables **M & F**

In particular, the form of the correlation line is very similar for both variables of the children **X3** and artificial quotients of intelligence vector **W** °

The general multidimensional correlation index (**ICMG**) is 17,77 and the highest determination coefficient **r²** of this graph is 0,89

Both values are very high and the **MCIG** is bigger than without evolution, which already was very high.

This graph has the highest **MCIG** of *The EDI Study.* Taking into account the difference with other rearrangement criterion it is possible to say that **M1F1** is the best of the four criterion. Even more, the second best criterion **R** ° and the third **X6 **seem to be less powerful as they differ more to the component **M1F1.**